Colour in Context
Research group
Computer Vision Center

Low-level Spatio-Chromatic Grouping for Saliency Estimation

IEEE Transaction on Pattern Analysis and Machine Intelligence - June 2013
IF: 5.694. area: COMPUTER SCIENCE, ARTIFICIAL I. Quartile: 1.
 
Download the publication : pami2013.pdf [3.6Mo]  
We propose a saliency model termed SIM (Saliency by Induction Mechanisms) which is based on a low-level spatio-chromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely non-parametric. SIM outperforms state-of-the-art methods in predicting eye-fixations on two datasets and using two metrics.

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BibTex references

@Article\{MVO2013,
  author       = "Naila Murray and Maria Vanrell and Xavier Otazu and C. Alejandro Parraga",
  title        = "Low-level Spatio-Chromatic Grouping for Saliency Estimation",
  journal      = "IEEE Transaction on Pattern Analysis and Machine Intelligence",
  month        = "June",
  year         = "2013",
  abstract     = "We propose a saliency model termed SIM (Saliency by Induction Mechanisms) which is based on a low-level spatio-chromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely non-parametric. SIM outperforms state-of-the-art methods in predicting eye-fixations on two datasets and using two metrics.",
  ifactor      = "5.694",
  quartile     = "1",
  area         = "COMPUTER SCIENCE, ARTIFICIAL I",
  url          = "http://cat.uab.cat/Public/Publications/2013/MVO2013"
}

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